






import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
   from transformers import (
       FlaxBertModel,
       FlaxCLIPVisionModel,
       FlaxVisionTextDualEncoderModel,
       FlaxViTModel,
       VisionTextDualEncoderConfig,
       VisionTextDualEncoderProcessor,
   )
   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )


if is_torch_available():
   import torch

   from transformers import VisionTextDualEncoderModel

if is_vision_available():
   from PIL import Image

def     snake_case   (UpperCAmelCase__    )			->							int:
  if isinstance(UpperCAmelCase__      ,       collections.abc.Iterable    ):
    return x
  return (x, x)




@require_flax
class 							_lowerCAmelCase:





       """simple docstring"""







       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         pass





       def      _a       (   self				):
         pass





       def      _a       (   self				):
         pass





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							np.abs((a - b)				).max()
         self.assertLessEqual(_lowerCamelCase    ,     _lowerCamelCase    ,     f'''Difference between torch and flax is {diff} (>= {tol}).'''				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_:   str        =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   Any        =							FlaxVisionTextDualEncoderModel(_lowerCamelCase				)

         UpperCamelCase_:   Optional[int]        =							model(input_ids=_lowerCamelCase    ,     pixel_values=_lowerCamelCase    ,     attention_mask=_lowerCamelCase				)

         self.assertEqual(output['text_embeds'].shape    ,     (input_ids.shape[0], config.projection_dim)				)
         self.assertEqual(output['image_embeds'].shape    ,     (pixel_values.shape[0], config.projection_dim)				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							self.get_vision_text_model(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							{'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_:   List[Any]        =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							model(input_ids=_lowerCamelCase    ,     pixel_values=_lowerCamelCase    ,     attention_mask=_lowerCamelCase				)

         self.assertEqual(output['text_embeds'].shape    ,     (input_ids.shape[0], model.config.projection_dim)				)
         self.assertEqual(output['image_embeds'].shape    ,     (pixel_values.shape[0], model.config.projection_dim)				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							self.get_vision_text_model(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   Optional[int]        =							{'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_:   Any        =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase				)

         UpperCamelCase_:   Optional[Any]        =							model(input_ids=_lowerCamelCase    ,     pixel_values=_lowerCamelCase    ,     attention_mask=_lowerCamelCase				)
         UpperCamelCase_:   Any        =							output[0]

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(_lowerCamelCase				)
           UpperCamelCase_:   Union[str, Any]        =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase				)

           UpperCamelCase_:   Any        =							model(input_ids=_lowerCamelCase    ,     pixel_values=_lowerCamelCase    ,     attention_mask=_lowerCamelCase				)
           UpperCamelCase_:   int        =							after_output[0]
           UpperCamelCase_:   Dict        =							np.amax(np.abs(out_a - out_a				)				)
           self.assertLessEqual(_lowerCamelCase    ,     1e-3				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							self.get_vision_text_model(_lowerCamelCase    ,     _lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							{'vision_model': vision_model, 'text_model': text_model}
         UpperCamelCase_:   Optional[int]        =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase				)

         UpperCamelCase_:   List[str]        =							model(
             input_ids=_lowerCamelCase    ,     pixel_values=_lowerCamelCase    ,     attention_mask=_lowerCamelCase    ,     output_attentions=_lowerCamelCase				)

         UpperCamelCase_:   List[str]        =							output.vision_model_output.attentions
         self.assertEqual(len(_lowerCamelCase				)    ,     vision_config.num_hidden_layers				)

         # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCamelCase_:   Any        =							to_atuple(vision_model.config.image_size				)
         UpperCamelCase_:   Union[str, Any]        =							to_atuple(vision_model.config.patch_size				)
         UpperCamelCase_:   int        =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         UpperCamelCase_:   Optional[int]        =							num_patches + 1
         self.assertEqual(vision_attentions[0].shape[-3:]    ,     (vision_config.num_attention_heads, seq_len, seq_len)				)

         UpperCamelCase_:   str        =							output.text_model_output.attentions
         self.assertEqual(len(_lowerCamelCase				)    ,     text_config.num_hidden_layers				)

         self.assertEqual(
             text_attentions[0].shape[-3:]    ,     (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])    ,     )





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         pt_model.to(_lowerCamelCase				)
         pt_model.eval()

         # prepare inputs
         UpperCamelCase_:   Optional[Any]        =							inputs_dict
         UpperCamelCase_:   Optional[int]        =							{k: torch.tensor(v.tolist()				) for k, v in flax_inputs.items()}

         with torch.no_grad():
           UpperCamelCase_:   Union[str, Any]        =							pt_model(**_lowerCamelCase				).to_tuple()

         UpperCamelCase_:   List[Any]        =							fx_model(**_lowerCamelCase				).to_tuple()
         self.assertEqual(len(_lowerCamelCase				)    ,     len(_lowerCamelCase				)    ,     'Output lengths differ between Flax and PyTorch'				)
         for fx_output, pt_output in zip(fx_outputs[:4]    ,     pt_outputs[:4]				):
           self.assert_almost_equals(_lowerCamelCase    ,     pt_output.numpy()    ,     4e-2				)

         # PT -> Flax
         with tempfile.TemporaryDirectory() as tmpdirname:
           pt_model.save_pretrained(_lowerCamelCase				)
           UpperCamelCase_:   int        =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase    ,     from_pt=_lowerCamelCase				)

         UpperCamelCase_:   List[Any]        =							fx_model_loaded(**_lowerCamelCase				).to_tuple()
         self.assertEqual(len(_lowerCamelCase				)    ,     len(_lowerCamelCase				)    ,     'Output lengths differ between Flax and PyTorch'				)
         for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]    ,     pt_outputs[:4]				):
           self.assert_almost_equals(_lowerCamelCase    ,     pt_output.numpy()    ,     4e-2				)

         # Flax -> PT
         with tempfile.TemporaryDirectory() as tmpdirname:
           fx_model.save_pretrained(_lowerCamelCase				)
           UpperCamelCase_:   Any        =							VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase    ,     from_flax=_lowerCamelCase				)

         pt_model_loaded.to(_lowerCamelCase				)
         pt_model_loaded.eval()

         with torch.no_grad():
           UpperCamelCase_:   int        =							pt_model_loaded(**_lowerCamelCase				).to_tuple()

         self.assertEqual(len(_lowerCamelCase				)    ,     len(_lowerCamelCase				)    ,     'Output lengths differ between Flax and PyTorch'				)
         for fx_output, pt_output_loaded in zip(fx_outputs[:4]    ,     pt_outputs_loaded[:4]				):
           self.assert_almost_equals(_lowerCamelCase    ,     pt_output_loaded.numpy()    ,     4e-2				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[int]        =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   int        =							VisionTextDualEncoderModel(_lowerCamelCase				)
         UpperCamelCase_:   str        =							FlaxVisionTextDualEncoderModel(_lowerCamelCase				)

         UpperCamelCase_:   List[Any]        =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,     _lowerCamelCase				)
         UpperCamelCase_:   List[str]        =							fx_state

         self.check_pt_flax_equivalence(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase    ,     _lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							VisionTextDualEncoderModel(_lowerCamelCase				)
         UpperCamelCase_:   str        =							FlaxVisionTextDualEncoderModel(_lowerCamelCase				)

         UpperCamelCase_:   List[Any]        =							load_flax_weights_in_pytorch_model(_lowerCamelCase    ,     fx_model.params				)

         self.check_pt_flax_equivalence(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   str        =							self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**_lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							self.prepare_config_and_inputs()
         self.check_save_load(**_lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**_lowerCamelCase				)





       @is_pt_flax_cross_test
       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self.prepare_config_and_inputs()
         UpperCamelCase_:   str        =							config_inputs_dict.pop('vision_config'				)
         UpperCamelCase_:   Dict        =							config_inputs_dict.pop('text_config'				)

         UpperCamelCase_:   int        =							config_inputs_dict

         self.check_equivalence_pt_to_flax(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)
         self.check_equivalence_flax_to_pt(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase				)





       @slow
       def      _a       (   self				):
         UpperCamelCase_ ,UpperCamelCase_:   int        =							self.get_pretrained_model_and_inputs()

         UpperCamelCase_:   Optional[Any]        =							model_a(**_lowerCamelCase				)
         UpperCamelCase_:   Any        =							outputs[0]

         with tempfile.TemporaryDirectory() as tmp_dirname:
           model_a.save_pretrained(_lowerCamelCase				)
           UpperCamelCase_:   str        =							FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase				)

           UpperCamelCase_:   str        =							model_a(**_lowerCamelCase				)
           UpperCamelCase_:   str        =							after_outputs[0]
           UpperCamelCase_:   Optional[Any]        =							np.amax(np.abs(out_a - out_a				)				)
           self.assertLessEqual(_lowerCamelCase    ,     1e-5				)







@require_flax
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             'hf-internal-testing/tiny-random-vit'    ,     'hf-internal-testing/tiny-bert'    ,     vision_from_pt=_lowerCamelCase    ,     text_from_pt=_lowerCamelCase    ,     )
         UpperCamelCase_:   List[Any]        =							1_3
         UpperCamelCase_:   Tuple        =							floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]				)
         UpperCamelCase_:   Union[str, Any]        =							ids_tensor([batch_size, 4]    ,     model.config.text_config.vocab_size				)
         UpperCamelCase_:   str        =							random_attention_mask([batch_size, 4]				)
         UpperCamelCase_:   Optional[int]        =							{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

         return model, inputs





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Any        =							FlaxViTModel(_lowerCamelCase				)
         UpperCamelCase_:   Dict        =							FlaxBertModel(_lowerCamelCase				)
         return vision_model, text_model





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							FlaxViTModelTester(self				)
         UpperCamelCase_:   Dict        =							FlaxBertModelTester(self				)
         UpperCamelCase_:   Tuple        =							vit_model_tester.prepare_config_and_inputs()
         UpperCamelCase_:   Optional[int]        =							bert_model_tester.prepare_config_and_inputs()

         UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							vision_config_and_inputs

         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							text_config_and_inputs

         # make sure that cross attention layers are added
         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": attention_mask,
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
         }







@require_torch
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   int        =							FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             'hf-internal-testing/tiny-random-clip'    ,     'hf-internal-testing/tiny-bert'    ,     vision_from_pt=_lowerCamelCase    ,     text_from_pt=_lowerCamelCase    ,     )
         UpperCamelCase_:   Union[str, Any]        =							1_3
         UpperCamelCase_:   Optional[int]        =							floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]				)
         UpperCamelCase_:   Optional[Any]        =							ids_tensor([batch_size, 4]    ,     model.config.text_config.vocab_size				)
         UpperCamelCase_:   Any        =							random_attention_mask([batch_size, 4]				)
         UpperCamelCase_:   List[str]        =							{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

         return model, inputs





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   str        =							FlaxCLIPVisionModel(_lowerCamelCase				)
         UpperCamelCase_:   int        =							FlaxBertModel(_lowerCamelCase				)
         return vision_model, text_model





       def      _a       (   self				):
         UpperCamelCase_:   int        =							FlaxCLIPVisionModelTester(self				)
         UpperCamelCase_:   Tuple        =							FlaxBertModelTester(self				)
         UpperCamelCase_:   List[str]        =							clip_model_tester.prepare_config_and_inputs()
         UpperCamelCase_:   int        =							bert_model_tester.prepare_config_and_inputs()

         UpperCamelCase_ ,UpperCamelCase_:   Optional[int]        =							vision_config_and_inputs

         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Union[str, Any]        =							text_config_and_inputs

         # make sure that cross attention layers are added
         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": attention_mask,
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
         }







@require_flax
@require_vision
class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       @slow
       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'    ,     logit_scale_init_value=1.0				)
         UpperCamelCase_:   Optional[Any]        =							VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'				)

         UpperCamelCase_:   Dict        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
         UpperCamelCase_:   Any        =							processor(
             text=['una foto di un gatto', 'una foto di un cane']    ,     images=_lowerCamelCase    ,     padding=_lowerCamelCase    ,     return_tensors='np'				)

         UpperCamelCase_:   Union[str, Any]        =							model(**_lowerCamelCase				)

         # verify the logits
         self.assertEqual(outputs.logits_per_image.shape    ,     (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])				)
         self.assertEqual(
             outputs.logits_per_text.shape    ,     (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])    ,     )

         UpperCamelCase_:   str        =							np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]				)

         self.assertTrue(np.allclose(outputs.logits_per_image    ,     _lowerCamelCase    ,     atol=1e-3				)				)
"""simple docstring"""


import os
from distutils.util import strtobool





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						for e in env_keys:
												lowerCamelCase__   =int(os.environ.get(__lowerCAmelCase  ,       -1							)							)
												if val >= 0:
																		return val
						return default





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase=False							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return strtobool(__lowerCAmelCase							) == 1  # As its name indicates `strtobool` actually returns an int...





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase="no"							)				->   Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__   =os.environ.get(__lowerCAmelCase  ,       str(__lowerCAmelCase							)							)
						return value


